Dr. Timothy W. Randolph, Ph.D. Mathematics, will participate in a period of mentored research and training at the University of Washington (UW) and the Fred Hutchinson Cancer Research Center (FHCRC). The focus of the research is the integration of engineering and mathematical techniques into the problems of interpreting extremely high-dimensional data from genomics and proteomics technologies. The training/mentoring goals are to obtain a significant biological background while developing biostatistical and computational proficiency in the context of biomedical data related to genetic and molecular indicators of disease, or biomarkers. The three co-sponsors have expertise ranging from laboratory-based molecular biology, to biostatistical methodology and application, to analysis of medical diagnostics and biomarker research. They are Dr. M. Pepe (UW and FHCRC) Professor of Biostatistics and co-investigator of the Data Management and Coordinating Center (DMCC) of the Early Detection Research Network (EDRN), Dr. Z. Feng (FHCRH and UW) the Principal Investigator of the DMCC, and Dr. P. Lampe (FHCRC and UW) Associate Research Professor in Pathobiology and cell biology. Experience will be gained through coursework at the UW, laboratory experience at the FHCRC involving microarray and spectrometry equipment, and quantitative research on biomarker identification within the DMCC and at UW. Data for the quantitative research will come from gene-expression microarray and protein mass spectrometry technologies. Sources of the data include: clinical data from the EDRN, laboratory data at the FHCRC, and existing data. Research will focus on analytic techniques for high-dimensional genomic and proteomic data in (i) classifying profiles as disease versus normal; (ii) identifying individual genes and proteins, or localized patterns, that are differently expressed across samples. Additional research will involve the dynamics of gene expression in cellular states. The methods used in the quantitative analysis will include mathematics and engineering techniques from functional analysis, signal processing, and optimization and estimation for high-dimensional systems. Experience from this period of collaboration, coursework, and mentoring will enable Dr. Randolph to play a leading quantitative role in multi-disciplinary scientific teams.